Abstract:

Wheat stripe rust is an important biological disaster that affects the safe production of wheat in China for a long time. The number of spores of wheat stripe rust is a direct factor affecting its pathogenesis and transmission. At present, it mainly relies on the field sampling and investigation of agricultural technicians to predict and forecast. It is time-consuming and laborious, and difficult to achieve long-term monitoring of diseases, thus affecting the accuracy of forecasting and the timeliness of prevention and control. The existing automatic spore monitoring device also has the problems that the collecting device is mostly in the form of manual replacement of slides, and the direct acquisition of components in the air by a limited area of the slide may result in inaccurate sample collection and too small sample size. In order to further improve the monitoring and forecasting ability of wheat stripe rust, a wheat stripe rust monitoring device was designed and implemented, which based on the internet to build a wheat stripe rust monitoring platform, and based on the embedded system to establish a complete set of wheat stripe rust spore collection and image transmission processing device. Spore acquisition was performed using a slide adsorption device of "Six prism column + Electromagnet + Microscope". Control the up and down movement of the electromagnet to control the up and down movement of the slide; update the slide by controlling the rotation of the hexagonal shaft; obtain the image by controlling the time synchronization of the microscope and the shaft; control the cleaning solvent the smear and the movement of the cleaning block enable the slide to be cleaned. At the same time, a spore counting program based on the server platform was designed to process and analyze the collected slide images. The spore counting program used in this design is based on Python 3.6 and combined with the Skimage image processing package for spore image analysis and processing. The geometry factor feature based method was used, and the number of spores in the microscope field was finally obtained based on the regional attribute values. The experimental results show that the platform server image processing algorithm can achieve accurate counting of spores, the accuracy of counting the test images is 100%; the success rate of the slide switching system is 95%.This study can lay a foundation for the real-time monitoring of wheat stripe rust in the field, and can also provide references for the monitoring of other airborne diseases in the field.

Key words: wheat stripe rust, internet, embedded system, remote monitoring, image processing, spores counting